Publicly available cytokine data: Limitations and opportunities
Authors: Debajyoti Ghosh, Tesfaye B. Mersha
Journal: Journal of Allergy and Clinical Immunology (2022)

Cytokines are low-molecular-weight intercellular signaling molecules. They regulate immune cell homeostasis by mediating innate and acquired immunity, and inflammation in human health and disease. The term cytokine was coined in 1974 to replace lymphokine—a term used in the late 1960's to denote lymphocyte-derived soluble proteins with immunologic functions. Since cytokines' discovery, their roles in allergy, asthma, and clinical immunology have been well recognized. Research on cytokines has gained significant momentum over the past few decades, leading to a large body of scientific literature and associated data sets in the public domain. Although studies on cell biology, T-cell polarization, and cytokine measurement experiments led to early growth of cytokine information, recent developments in this field have resulted largely from omics, high-throughput screening, ancestry, and cohort studies. Leveraging cytokine data systematically will be helpful in identifying cytokine biomarkers and disease endotypes and will accelerate additional cytokine-targeted therapies. This Paradigms and Perspectives article will highlight some of the opportunities of cytokine data mining, associated challenges, and limitations thereof. For biocuration (defined as organizing, representing, and making biologic information accessible), extracting information from existing literature is highly dependent on precise biologic terms that provide indices into the literature and across biologic databases. In case of information on cytokines, however, because of rapid growth in the field and with the fast-evolving nomenclature and terminology used in the relevant literature, a careful curation process is warranted. We have noted that historically the terms cytokines and interleukins have often been used interchangeably to indicate their roles as signaling molecules between lymphocytes, whereas several authors have used terms such as monokines, lymphokines (cytokines produced by monocytes and lymphocytes, respectively), or chemokine and interferon (cytokines with chemoattractant and antiviral functions, respectively). Universal numeric nomenclature for more recently identified cytokines has allowed cataloging, storage, and systematic retrieval of existing cytokine data. Collectively, the term cytokine has often been used as an umbrella term that includes chemokines, interferons, interleukins, lymphokines, growth factors, and TNFs.1Zhang J.M. An J. Cytokines, inflammation, and pain.Int Anesthesiol Clin. 2007; 45: 27-37Crossref PubMed Scopus (1448) Google Scholar Cytokine data can be directly helpful for clinicians. The huge volume of data generated during the ongoing severe acute respiratory distress syndrome–coronavirus 2 (SARS-CoV-2) pandemic, including data from cytokine panels, represents a striking and meaningful recent example, as anticytokine therapies have been proven efficacious for treatment.2Batista C.M. Foti L. Anti-SARS-CoV-2 and anti-cytokine storm neutralizing antibody therapies against COVID-19: update, challenges, and perspectives.Int Immunopharmacol. 2021; 99108036Crossref PubMed Scopus (9) Google Scholar In a population of more than 1400 hospitalized patients with coronavirus disease 2019 (COVID-19) within the Mount Sinai Health System in New York City, serum IL-6 and TNF-α levels at the time of hospitalization were independent and significant predictors of COVID-19 severity and death, as was further validated in a second cohort of patients demonstrating the utility of cytokine data in identifying patients at risk for development of severe disease or death.3Del Valle D.M. Kim-Schulze S. Huang H.H. Beckmann N.D. Nirenberg S. Wang B. et al.An inflammatory cytokine signature predicts COVID-19 severity and survival.Nat Med. 2020; 26: 1636-1643Crossref PubMed Scopus (1016) Google Scholar Therefore, cytokine data can be used to identify biomarkers, whereas a patient's cytokine profile can be matched against curated cytokine databases to identify disease risks. More than 35 years ago, it was proposed that CD4+ TH cells could have 2 distinct phenotypes, named TH1 and TH2 cells, based on the production of IFN-γ and IL-4, respectively. Since then, TH cell functional subtypes have been expanded to include populations such as IFN-γ–producing TH1 cells, IL-4/IL-5/IL-13–producing TH2 cells, IL-9–producing TH9 cells, IL-17–producing TH17 cells, IL-22–producing TH22 cells, IL-21–producing T follicular helper (TFH) cells, and IL-10/TGF-β/IL-35–producing regulatory T-cell subpopulations—each contributing significantly to the human cytokine repertoire and playing important roles in human diseases. TH2 cell–related cytokines are hallmarks of allergic diseases, including allergic asthma, whereas TH17 cytokines are associated with nonatopic asthma and other severe neutrophilic diseases. In addition to being reported for CD4+ cells, cytokine polarization has also been reported for CD8+ cells, whereas other cell populations such as innate lymphoid cells and natural killer cells can also be significant sources of these cytokines. The development of endotype-targeted personalized therapies has been advanced greatly on the basis of cytokine patterns and can be critical for precision management of asthma. For example, in childhood asthma, distinct cytokine cluster–based endotypes and their association with diminished lung function and allergic sensitization were demonstrated (in the CLARA/CLAUS study in Germany).4Salvermoser M. Zeber K. Boeck A. Klucker E. Schaub B. Childhood asthma: novel endotyping by cytokines, validated through sensitization profiles and clinical characteristics.Clin Exp Allergy. 2021; 51: 654-665Crossref PubMed Scopus (5) Google Scholar Similarly, plasma cytokine values were used in differentiating children with food allergy from children without allergy who were sensitized to peanut and egg (the HealthNuts study [Australia]).5Dang T.D. Tang M.L. Koplin J.J. Licciardi P.V. Eckert J.K. Tan T. et al.Characterization of plasma cytokines in an infant population cohort of challenge-proven food allergy.Allergy. 2013; 68: 1233-1240Crossref PubMed Scopus (24) Google Scholar In this cohort, food sensitization was significantly associated with higher plasma IL-4, IL-13, and IL-12p70 levels and lower IL-10 levels when compared with nonsensitized controls, whereas higher IL-10 and IL-6 levels were found in food-sensitized infants when compared with allergic subjects. Similarly, cytokine-based endotype clustering has been proposed for atopic dermatitis and eosinophilic esophagitis. For example, in the case of atopic dermatitis, 4 clusters were previously proposed: clusters associated with high levels of serum pulmonary and activation-regulated chemokine (PARC) (cluster 1), low levels of IFN-α and vascular endothelial growth factor (cluster 2), low levels of IFN-β and IL-1 (cluster 3), and high levels of IL-1, IL-4, IL-13, and thymic stromal lymphopoietin (TSLP) (cluster 4). Importantly, the successful use of cytokine-targeted therapies requires identifying the proper disease endotype to target. For example, patients with type 2–high asthma are more likely to benefit from dupilumab, which targets the IL-4 receptor α-chain common to both IL-4 and IL-13 receptors, and mepolizumab (for type 2 eosinophilic asthma), which binds IL-5. Therefore, cytokine data and/or databases would be very helpful for identification of disease endotypes and rational designing of treatment regimens. Although systematic analyses of plasma and serum cytokine levels have already targeted a range of autoimmune and inflammatory processes, normative data are necessary to establish a baseline for cytokine levels against which the measurement can be compared. In an attempt to address this need, plasma cytokines from a cohort of well-screened healthy subjects (N = 126) covering a wide range of age, sex, and racial backgrounds were analyzed by using Luminex assay, and reference ranges for multiple cytokines were determined. Raw and plotted data have been made available publicly (https://duke-hhis.github.io/reference-range/#/ [accessed June 24, 2022]). The role of environmental and nongenetic host factors influencing cytokine responses in healthy subjects (N = 534) of Western European ancestry has been studied in the Human Functional Genomics Project (HFGP) (www.humanfunctionalgenomics.org [accessed July 27, 2022]). The results indicated that IFN-γ and IL-22 responses decrease with age. Additionally, sex and seasonality can influence host cytokine responses. Moreover, data derived from the HFGP have demonstrated a strong impact of genetic ancestry on cytokine expression following exposure to bacterial, fungal, viral, and nonmicrobial stimuli in healthy human cohorts. Collectively, the results captured the effects of age, sex, genetic ancestry, and cellular heterogeneity on the variability of human immune functions, particularly via cytokine responses. The availability of big data sets (eg, those resulting from large projects such as HFGP) would greatly help progress in informatics related to cytokine biology. Ancestry information, geographic location data, and parasite infection status should be reported with cytokine data. It is widely accepted that a broad range of factors contribute to functional differences in human immunity, and several population genetic studies have attempted to understand whether population differences in cytokine genes contribute to disease disparity across geographic ancestry. Studies have highlighted the differences in TH cell frequencies in African populations versus in their age-matched European counterparts, which can be attributed to evolutionary adaptation associated with between-population differences in allele frequencies. In addition to ethnicity, gross immune differences have been reported between rural and urban populations, as well as in parasite and/or helminth infection status. Several genome-wide association studies (GWASs) have identified and confirmed cytokine single-nucleotide polymorphism (SNPs) associated with allergic diseases, with the most replicated cytokine genes being IL4, IL13, IL33, and TSLP (GWAS catalog [https://www.ebi.ac.uk/gwas/]). However, because current results are limited primarily to individuals of European ancestry, multiethnic studies are required to overcome this limitation. Advances in high-throughput sequencing have led to the discovery of thousands of cytokine SNPs associated with diseases. The amount and complexity of the data poses serious challenges to biologists and clinicians with limited bioinformatics expertise. Once a genome-wide study has been performed and statistically significant SNPs have been identified, the investigator can explore data repositories such as the National Center for Biotechnology Information, Ensembl, and University of California Santa Cruz portals to obtain information about prioritized SNPs. There is no single database to comprehensively capture potentially interesting variants. The PUBMED Entrez search site (https://www.ncbi.nlm.nih.gov/search/) is usually the first stop to obtain information on whether a cytokine-associated SNP is in a gene and whether there are reported genotypic and allelic frequencies for major population groups. The University of California Santa Cruz Genome Browser (http://genome.ucsc.edu/) provides excellent information about the features of the genome in a particular region. The GWAS Catalog resource is a useful tool to determine what is already known about a specific SNP, genes, and phenotype in terms of disease associations. The 2 major public transcriptomic data repositories, Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) and ArrayExpress (http://www.ebi.ac.uk/microarray-as/ae), house gene expression data that can used to assess cytokine gene expression. Next, statistically ranked enriched pathways can be identified from genomic and transcriptomic data. Ranked pathways are used to explore enriched biological processes associated with cytokine-related genes and SNPs by using current knowledge bases (eg, gnomAD, Gene Ontology, ClinVar, and The Human Gene Mutation Database). Finally, prioritized genes and SNPs can be validated in animal models or in human samples. Bioinformatics tools such as GENEASE can be applied to identify potential functional enrichment, variant frequency in various ancestral populations, and what else may already be known. GENEASE is a web-based application to automatically extract genes or SNPs information based on user-defined criteria and export results in formats suitable for subsequent use (http://research.cchmc.org/mershalab/GENEASE/explore.html). However, the available databases listed in this article and elsewhere may also include genes and SNPs associated with cytokine/chemokine-receptor interactions, which might be considered during downstream informatics related to cytokine biology. The profound roles of environment and genetic ancestry and their interactions in cytokine responses have been further highlighted in the case of diseases, including COVID-19.6Ghosh D. Bernstein J.A. Mersha T.B. COVID-19 pandemic: the African paradox.J Glob Health. 2020; 10020348Crossref Scopus (29) Google Scholar European ancestry is associated with a strong and quick postinfection cell type–specific type I interferon response predictive of lower viral titers.7Randolph H.E. Fiege J.K. Thielen B.K. Mickelson C.K. Shiratori M. Barroso-Batista J. et al.Genetic ancestry effects on the response to viral infection are pervasive but cell type specific.Science. 2021; 374: 1127-1133Crossref PubMed Scopus (14) Google Scholar Human adaptation and coevolution with viruses and other pathogens have induced a strong immunogenetic selection causing divergent cytokine responses. For example, the Fulani population of West Africa has fewer malaria cases, a lower prevalence of Plasmodium infection, and altered TH1/TH2 cytokine responses compared with its geographic neighbors. All of these factors, along with vaccination status, can influence host cytokine responses and should be documented as part of a cytokine-focused database. Currently, there are a limited number of databases dedicated to cytokines. A master list cytokine registry collected from several resources, including MeSH (Medical Subject Headings), Protein Ontology, EntrezGene, HGNC, MGI, and UniProt, can be found at https://www.immport.org/resources/cytokineRegistry (accessed on July 27, 2022). Online tools such as CytokineExplore (http://al-saleh.cc/exabx.com/apps/cytokineexplore/ [accessed July 27, 2022]) can help to explore cytokine data sets and general exploratory box plots, histograms, principal component analysis and partial least squares discriminant analysis plots, t tests and Mann-Whitney statistical tests, and heatmaps for comparing groups. Bioinformatic resources such as CytokineLink (https://github.com/korcsmarosgroup/CytokineLink [accessed May 6, 2022]) can model cytokine-mediated intercellular network interactions. CytokineLink is based on the integration of data from various interaction and expression databases and literature. In spite of a growing number of cytokine data sets in the public domain, wide clinical use of these data sets has not yet been achieved owing to the multiple challenges outlined here. First, sample collection, storage and analysis protocols vary between investigators and/or laboratories, making the results difficult to compare. Several authors have pointed out the effects of sample handling, including sample collection (serum, EDTA plasma/citrate plasma), processing, sample storage condition, handling time, source biologic fluids (serum, plasma, saliva, etc, which contain variable amounts of cytokines, cytokine-binding proteins, and soluble cytokine receptors), and freeze-thaw cycles (reviewed extensively by Liu et al 2021).8Liu C. Chu D. Kalantar-Zadeh K. George J. Young H.A. Liu G. Cytokines: from clinical significance to quantification.Adv Sci (Weinh). 2021; 8e2004433Google Scholar Furthermore, a wide range of cytokine half-lives can differentially affect the precision of cytokine measurement, analyses, and interpretations. For example, the reported half-life of IL-6 is 15.5 hours, whereas the half-life of IL-4 is only 20 minutes. Therefore, for accurate and reproducible data reporting, the conditions related to sample collection and handling should be reported with the data considering major factors such as thermostability of cytokines in body fluids (eg, plasma/ serum), the influence of sample handing conditions and freeze-thaw cycles on cytokine levels (extensively reviewed by Simpson et al 2020),9Simpson S. Kaislasuo J. Guller S. Pal L. Thermal stability of cytokines: a review.Cytokine. 2020; 125154829Crossref PubMed Scopus (25) Google Scholar and the effects of soluble cytokine receptors - all of which may affect the reliability of cytokine measurements. Second, the effect of assay method and/or platform is also well documented. The most commonly used methods for cytokine quantification are ELISA, which can be reliable but time-consuming, depends heavily on expensive laboratory instruments, requires trained human resources, and is characterized by significant handling time and complexity. There are also a number of methods that are used for cytokine measurements, such as multiplex bead immunoassay and magnetic bead–based multiplex immunoassay (Luminex). Although cytokine measurements are increasingly becoming a part of the routine diagnostics and immune monitoring, several commercial assays and platforms are now becoming available to capture cytokine levels in biofluids. However, there is currently little standardization across analytic platforms/methods and laboratories, as was recently emphasized in the College of American Pathologists' survey report.10Knight V. Long T. Meng Q.H. Linden M.A. Rhoads D.D. Variability in the laboratory measurement of cytokines.Arch Pathol Lab Med. 2020; 144: 1230-1233Crossref PubMed Scopus (10) Google Scholar The investigators compared the 5 available methods of cytokine quantitation, namely, ELISA, chemiluminescence, electrochemiluminescence, multiplex bead immunoassay, and magnetic bead–based multiplex immunoassay. Statistically significant differences in values owing to platforms were found for IL-6, IL-8, and TNF-α depending on the concentrations of analytes. Traditional methods such as ELISA have limited applicability in terms of measuring multiple cytokines in real time, and they can be unsuitable in many emergency situations. Therefore, novel biosensor-based sensitive, selective, and rapid real-time cytokine analysis platforms are being developed. However, these platforms have different linear detection ranges, leading to different ranges of accuracy, which can also depend on particular cytokine analyte(s). The performance of the commercially available cytokine kits with their linearity ranges was reviewed extensively by Liu et al 2021.8Liu C. Chu D. Kalantar-Zadeh K. George J. Young H.A. Liu G. Cytokines: from clinical significance to quantification.Adv Sci (Weinh). 2021; 8e2004433Google Scholar In summary, although cytokine data related to human health and disease are being increasingly available and applied to human health and disease, systematic dissemination of those data sets has not yet been achieved. The utility of high-quality clinical databases has long been recognized. High-throughput methodologies developed in the postgenomic era have generated high-volume omics data sets that are publicly available via well-maintained databases. Leveraging those data sets has led to significant discoveries, including personalized, predictive, and targeted medicine. As of now, however, dedicated cytokine data in the public domain and dissemination thereof to clinicians and researchers are largely lacking. Establishment of cytokine database(s) searchable by context (eg, disease, condition, sample type), assay platform (singleplex vs multiplexed), collection and/or handling method, sex, ancestry, and age is urgently needed (an overall outline is shown in Fig 1). This article is a call for a collaborative effort among clinicians, researchers, industry, and policymakers to develop publicly accessible high-quality cytokine database(s), which would greatly help in the development of personalized and predictive medicine.